AI 中文总结
本研究对UF³势进行基准测试，发现其精度可媲美多种主流MLIPs，能预测部分简单金属熔点且可可视化相互作用，为快速可解释MLIPs的应用提供了参考。
AI 中文摘要
机器学习原子间势（MLIPs）已成为分子动力学模拟中密度泛函理论（DFT）的强大替代方案，其计算成本仅为DFT的一小部分，却能达到接近DFT的精度。然而，许多最先进的MLIPs仍存在计算要求高、作为黑盒模型缺乏物理可解释性的问题，限制了其应用。本研究评估了超快速力场（UF³）势，该势采用带有三次B样条基的线性回归来表示有效的两体和三体相互作用。研究表明，UF³的精度可与GAP、MTP、NNP（Behler Parrinello）和qSNAP等成熟模型相媲美。进一步通过计算六种元素体系的熔点来研究UF³的可迁移性，这些体系的势在拟合时未使用任何固-液界面构型或关于熔化的显式热力学信息。该模型对简单金属（Ni、Cu、Li）的熔点预测与实验值的偏差在约6%以内，但对Mo和Si的熔点预测显著低估，且无法为Ge生成稳定的势，这反映了固定的三体截断展开在处理具有强角键或共价键的体系时存在局限性。此外，研究还展示了UF³基于样条的公式如何允许直接可视化学习到的相互作用，从而能够识别黑盒方法常掩盖的非物理行为。
英文摘要
Machine learning interatomic potentials (MLIPs) have emerged as a powerful alternative to density functional theory (DFT) for molecular dynamics simulations, offering near-DFT accuracy at a fraction of the computational cost. However, many state-of-the-art MLIPs remain computationally demanding and act as black boxes, limiting physical interpretability. In this work, we evaluate the ultra-fast force field (UF$^3$) potential, which employs linear regression with cubic B-spline basis to represent effective two- and three-body interactions. We show that UF$^3$ displays accuracy comparable to established models such as GAP, MTP, NNP (Behler Parrinello), and qSNAP MLIPs. We further investigate the transferability of UF$^3$ by computing melting points for six elemental systems with potentials fitted without any solid-liquid interface configurations or explicit thermodynamic information about melting. The model reproduces experimental melting points within $\sim$6% for simple metals (Ni, Cu, Li), but substantially underestimates them for Mo and Si and fails to yield a stable potential for Ge, reflecting the limitations of a fixed expansion truncated at the three-body term for systems with strong angular or covalent bonding. We further illustrate how UF$^3$'s spline-based formulation allows direct visualization of the learned interactions, enabling identification of unphysical behavior that black-box approaches often obscure.
Comments22 pages, 6 figures
